Hasil untuk "Chemical technology"

Zhanyun Wang, G. Walker, D. Muir et al.

Chemicals, while bringing benefits to society, may be released during their lifecycles and possibly cause harm to humans and ecosystems. Chemical pollution has been mentioned as one of the planetary boundaries within which humanity can safely operate, but is not comprehensively understood. Here, 22 chemical inventories from 19 countries and regions are analyzed to achieve a first comprehensive overview of chemicals on the market as an essential first step toward a global understanding of chemical pollution. Over 350,000 chemicals and mixtures of chemicals have been registered for production and use, up to three times as many as previously estimated and with substantial differences across countries/regions. A noteworthy finding is that the identities of many chemicals remain publicly unknown because they are claimed as confidential (over 50,000) or ambiguously described (up to 70,000). Coordinated efforts by all stakeholders including scientists from different disciplines are urgently needed, with (new) areas of interest and opportunities highlighted here.

S. Ahmed, M. Mofijur, Samiha Nuzhat et al.

Emerging contaminants (ECs) in wastewater have recently attracted the attention of researchers as they pose significant risks to human health and wildlife. This paper presents the state-of-art technologies used to remove ECs from wastewater through a comprehensive review. It also highlights the challenges faced by existing EC removal technologies in wastewater treatment plants and provides future research directions. Many treatment technologies like biological, chemical, and physical approaches have been advanced for removing various ECs. However, currently, no individual technology can effectively remove ECs, whereas hybrid systems have often been found to be more efficient. A hybrid technique of ozonation accompanied by activated carbon was found significantly effective in removing some ECs, particularly pharmaceuticals and pesticides. Despite the lack of extensive research, nanotechnology may be a promising approach as nanomaterial incorporated technologies have shown potential in removing different contaminants from wastewater. Nevertheless, most existing technologies are highly energy and resource-intensive as well as costly to maintain and operate. Besides, most proposed advanced treatment technologies are yet to be evaluated for large-scale practicality. Complemented with techno-economic feasibility studies of the treatment techniques, comprehensive research and development are therefore necessary to achieve a full and effective removal of ECs by wastewater treatment plants.

E. Perry

C. Parra-Cabrera, Clément Achille, S. Kuhn et al.

Fidele Benimana, Christopher Kucha, Anupam Roy et al.

ABSTRACT The global demand for edible flowers has increased due to their diverse applications in food, nutraceuticals, and the medical field. However, issues of species identification, adulteration, contamination, and quality necessitate the use of advanced methods to authenticate product quality for edible flowers. Conventional methods are expensive, time‐consuming, and require highly skilled personnel and technical expertise. Spectroscopic methods, including Fourier transform infrared, near‐infrared, and Raman spectroscopy, are efficient, fast, and non‐destructive, providing rapid insight into the chemical structure and authenticity of edible flowers. This review systematically summarizes the recent advances in spectroscopic methods for authenticating edible flowers, including the detection of chemical changes and ensuring product integrity. The primary goal is to examine the applications of spectroscopic techniques for assessing quality changes in edible flowers during processing for food applications. Spectroscopic techniques, such as FT‐IR, NIR, and Raman spectroscopy, are rapid, accurate, and non‐destructive alternatives for authenticating the composition and quality of edible flowers. These methods enable the detection of bioactive compounds, differentiation of species, and identification of adulterants with minimal sample processing. Furthermore, chemometric models enhance data analysis, allowing for automated classification and real‐time quality monitoring of edible flowers.

Shibin Li

This study examines the crystallization kinetics of Ni _50−x Mn _39 Sn _11 Fe _x (x = 0, 0.5, 2, 4 at.%) amorphous thin films prepared by DC magnetron sputtering. SEM and XRD confirm their amorphous structure. Non-isothermal DSC results show that the crystallization peak temperature increases from 542.7 K to 568.0 K as Fe content rises, while the apparent activation energy increases from 96.69 to 152.93 kJ mol ^−1 , indicating enhanced resistance to crystallization. Isothermal analysis yields Avrami exponents of 1.15–1.41 (average ≈1.2), corresponding to diffusion-controlled one-dimensional growth. Local activation-energy evaluation further reveals composition-dependent differences in nucleation and growth during various stages. These quantitative kinetic parameters clarify the role of Fe in altering crystallization behavior and support the optimization of annealing conditions for Ni-Mn-Sn-based functional thin films.

Xuezhi Bian, Emily A. Carter

Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving training and computational efficiency of machine learned interatomic potentials. We demonstrate the framework on Ca2+-CO32- ion pairing in aqueous solution, a key process underlying CO2 mineralization in seawater. As proof of principle, we first show that finetuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to large-scale simulations of complex aqueous and interfacial chemical processes.

Janine Wesslowski, Sadia Safi, Michelle Rottmann et al.

Upon engagement of one of the nineteen secreted Wnt signaling proteins with one of the ten Frizzled transmembrane Wnt receptors (FZD_1–10), a wide variety of cellular Wnt signaling responses can be elicited, the selectivity of which depends on the following: (1) the specific Wnt-FZD pairing, (2) the participation of Wnt co-receptors and (3) the cellular context. Co-receptors play a pivotal role in guiding the specificity of Wnt signaling, most notably between β-catenin-dependent and -independent pathways, where co-receptors such as LRP5/6 and ROR1/2/PTK7 play major roles, respectively. It remains less understood how specific Wnt/FZD combinations contribute to the selectivity of downstream Wnt signaling, and we lack accurate comparative data on their binding properties under physiological conditions. Here, using fluorescently tagged Wnt3a, Wnt5a and Wnt16 proteins and cell lines expressing HiBiT-tagged Frizzled, we build on our ongoing efforts to provide a complete overview of the biophysical properties of all Wnt/FZD interactions using full-length proteins. Our real-time NanoBRET analysis using living cells expressing low receptor levels provides more accurate quantification of binding and will help us understand how these binary engagements control Wnt signaling outputs. We also provide evidence that LRP6 regulates the binding affinity of Wnt/FZD interactions in the trimeric Wnt-FZD-LRP6 complex.

Majid Riaz, Pedro Guerra, Raffaele Gravina

This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles.

Heng Zhang, Jibin Zhou, Feiyang Xu et al.

The development of chemical engineering technology is a multi-stage process that encompasses laboratory research, scaling up, and industrial deployment. This process demands interdisciplinary col laboration and typically incurs significant time and economic costs. To tackle these challenges, we have developed a system based on ChemELLM in this work. This system enables users to interact freely with the chem ical engineering model, establishing a new paradigm for AI-driven in novation and accelerating technological advancements in the chemical sector.If you would like to experience our system, please visit our official website at: https://chemindustry.iflytek.com/chat.

Pavlo Kozub, Nataliia Yilmaz, Svitlana Kozub

The study demonstrates the capabilities of a vector-based approach for calculating stoichiometric coefficients in chemical equations, using black powder as an illustrative example. A method is proposed for selecting and constraining intermediate interactions between reactants, as well as for identifying final products. It is shown that even a small number of components can lead to a large number of final and intermediate products. Through concrete calculations, a correlation is established between the number of possible chemical equations and the number of reactants. A methodology is proposed for computing all possible chemical equations within a reaction system for arbitrary component ratios, enabling the derivation of all feasible chemical reactions. Additionally, a method is developed for calculating the chemical composition for a fixed set of reactants, allowing for the evaluation of the set of products resulting from all possible chemical interactions given a specified initial composition.

Sarah K. Mann, Angus Cowley-Semple, Emma Bryan et al.

Optical detection of magnetic resonance enables spin-based quantum sensing with high spatial resolution and sensitivity-even at room temperature-as exemplified by solid-state defects. Molecular systems provide a complementary, chemically tunable, platform for room-temperature optically detected magnetic resonance (ODMR)-based quantum sensing. A critical parameter governing sensing sensitivity is the optical contrast-i.e., the difference in emission between two spin states. In state-of-the-art solid-state defects such as the nitrogen-vacancy center in diamond, this contrast is approximately 30%. Here, capitalizing on chemical tunability, we show that room-temperature ODMR contrasts of 40% can be achieved in molecules. Using a nitrogen-substituted analogue of pentacene (6,13-diazapentacene), we enhance contrast compared to pentacene and, by determining the triplet kinetics through time-dependent pulsed ODMR, show how this arises from accelerated anisotropic intersystem crossing. Furthermore, we translate high-contrast room-temperature pulsed ODMR to self-assembled nanocrystals. Overall, our findings highlight the synthetic handles available to optically readable molecular spins and the opportunities to capitalize on chemical tunability for room-temperature quantum sensing.

T. Mendiara, F. García-Labiano, A. Abad et al.

In order to limit the increase in the global average temperature to 2 °C or below, the Paris Agreement proposed the reduction of CO2 emissions throughout this century. Bioenergy with CO2 capture and storage (BECCS) technologies represent an interesting option in order to allow this goal to be metgoal, because they are able to achieve negative CO2 emissions. Chemical looping (CL) is recognized as one of the most innovative CO2 capture technologies owing to its low energy penalty. CL processes permit the utilization of renewable fuels in a nitrogen-free atmosphere, given that the required oxygen is supplied by solid oxygen carriers. The present work presents an overview of the status of development of the use of biofuels in chemical looping technologies, including chemical looping combustion (CLC) and chemical looping with oxygen uncoupling (CLOU) for the production of heat/electricity, as well as chemical looping reforming (CLR), chemical looping gasification (CLG) and chemical looping coupled with water splitting (CLWS) for syngas/H2 generation. The main milestones in the development of such processes are shown, and the future trends and opportunities for CL technology with biofuels are discussed.

Asmae El Maangar, Clément Fleury, Stéphane Pellet-Rostaing et al.

We show hereby that recycling of NdFeB permanent magnets by selective leaching and precipitation is possible, using an electrolyte as hydrotrope, thus avoiding the need of any specific extractant molecules. We analyse the yield of the extractant-free process and show that the non toxic formulation of Sodium Salicylate and ethylacetate used as diluent and choosing the optimal tie-line in a ternary phase diagram allows extraction using any type of acid in the aqueous phase. Iron is well separated from rare earths and the product can be recovered directly form the fluid used in separation by oxalic acid precipitation.

Shengyan Wang, Huijia Dai, Qingling Tang et al.

Pichia pastoris is one of the most commonly used hosts for producing heterologous proteins, whereas production levels vary depending on the protein of interest and are also regulated by regulatory factors. We conducted RNA-seq by expressing the reporter EGFP and observed significant upregulation of certain subunits (Sec61p, Sbh1p, Sss1p, Sec66p and Sec72p) of the Sec complex in the high-expression recombinant GS115 stains. The overexpression of these genes may increase the expression levels of heterogeneous proteins. In this study, the endogenous promoters of the Sec complex subunits Sbh1p, Sss1p, Sec66p and Sec72p were isolated and verified their activity using the Lac-Z reporter gene. Sss1, Sbh1, Sec66 and Sec72 were overexpressed under the control of their own promoters in Pichia pastoris, respectively. The overexpression of Sss1, Sbh1, Sec66 and Sec72 in cells was confirmed by fluorescent microscope and Western blot analysis. The α-amylase was employed to evaluate the effect of overexpression of the Sec subunits on the heterologous protein expression. The results demonstrated that the α-amylase activity increased by 16%, 58%, 16% and 17% in the strains overexpressing Sss1, Sbh1, Sec66 and Sec72, respectively. Engineering the protein translocation pathway can be an alternative to enhance heterogeneous proteins in Pichia pastoris expression system.

Lucas R. Timmerman, Shashikant Kumar, Phanish Suryanarayana et al.

We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 elements, we demonstrate that the number of density functional theory calls remains approximately independent of the number of chemical elements, in contrast to the increase in the smooth overlap of atomic positions scheme.

Feng Wen, Tianyiyi He, Huicong Liu et al.

Abstract The past few years have witnessed the rapid innovation of wearable chemical sensors that induce impacts on various areas of our daily life. As the emerging Internet of Thing (IoT), wearable chemical sensors hold considerable promise not only for healthcare and fitness applications but also for other diversified applications ranging from environment monitoring to security/forensic identification. With the vision of more versatile, convenient, communicating and integrated, this review provides a comprehensive overview of the recent progress of wearable chemical sensors and systems. First, in terms of healthcare application, the development of wearable chemical sensors for markers detection from sweat is reviewed in the aspect of transduction mechanism and structural configuration (i.e., soft/soft-hard integration patch, tattoo, and microfluidics). Then the evolution of wearable tear and saliva sensors from simple sensor design to integrated system (e.g., communication module and power supply integration) are reviewed. Next, treatment (i.e., drug delivery), as an indispensable part of close loop sensing-therapeutic system establishment, is reviewed via the representatives of microneedle technology. Additionally, the progress of wearable chemical sensors for environmental monitoring and security/forensic applications are presented, showing the development of self-sustainable/on-site testing systems. With this review, we believe the development trend of wearable chemical sensors is towards multifunctional, sensing-therapeutic, self-powered and integrated systems.

M. Hupa, O. Karlström, E. Vainio

Abdullahi Chowdhury, Gour Karmakar, Joarder Kamruzzaman et al.

The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster–Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon’s entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.

Yulin Shen, Ruwei Huang, Bei Hua et al.

Tree height is a crucial structural parameter in forest inventory as it provides a basis for evaluating stock volume and growth status. In recent years, close-range photogrammetry based on smartphone has attracted attention from researchers due to its low cost and non-destructive characteristics. However, such methods have specific requirements for camera angle and distance during shooting, and pre-shooting operations such as camera calibration and placement of calibration boards are necessary, which could be inconvenient to operate in complex natural environments. We propose a tree height measurement method based on three-dimensional (3D) reconstruction. Firstly, an absolute depth map was obtained by combining ARCore and MidasNet. Secondly, Attention-UNet was improved by adding depth maps as network input to obtain tree mask. Thirdly, the color image and depth map were fused to obtain the 3D point cloud of the scene. Then, the tree point cloud was extracted using the tree mask. Finally, the tree height was measured by extracting the axis-aligned bounding box of the tree point cloud. We built the method into an Android app, demonstrating its efficiency and automation. Our approach achieves an average relative error of 3.20% within a shooting distance range of 2–17 m, meeting the accuracy requirements of forest survey.